class_docstr: Base class for change point datasets that are stored in a local file.
  
  Datasets that are part of the Alan Turing Institute Change Point Detection project.
  
  Parameters
  ----------
  annotations
      The file's associated annotations.
  desc
      Extra dataset parameters to pass as keyword arguments.
docstr: The function `annotations_aggregated` takes an annotator aggregation method as input and returns
  the aggregated annotations based on that method.
  
  Parameters
  ----------
  annotator_aggregation
      The parameter "annotator_aggregation" is a string that specifies the method of aggregating
  annotations from different annotators. It can take one of the following values:
  
  Returns
  -------
      the aggregated annotations based on the specified annotator aggregation method.
docstr_tok: ["The","function","`","annotations_aggregated","`","takes","an","annotator","aggregation","method","as","input","and","returns","the","aggregated","annotations","based","on","that","method",".","Parameters","--","--","--","--","--","annotator_aggregation","The","parameter","``","annotator_aggregation","''","is","a","string","that","specifies","the","method","of","aggregating","annotations","from","different","annotators",".","It","can","take","one","of","the","following","values",":","Returns","--","--","--","-","the","aggregated","annotations","based","on","the","specified","annotator","aggregation","method","."]
code: def _annotations_aggregated(self, annotator_aggregation):
      """The function `annotations_aggregated` takes an annotator aggregation method as input and returns
      the aggregated annotations based on that method.
  
      Parameters
      ----------
      annotator_aggregation
          The parameter "annotator_aggregation" is a string that specifies the method of aggregating
      annotations from different annotators. It can take one of the following values:
  
      Returns
      -------
          the aggregated annotations based on the specified annotator aggregation method.
  
      """
      if annotator_aggregation == "union":
          annotations = set()
          for annotator in self._annotations:
              annotations.update(self._annotations[annotator])
      elif annotator_aggregation == "intersection":
          annotations = set(self._annotations[0])
          for annotator in self._annotations:
              annotations.intersection_update(
                  self._annotations[annotator]
              )
      elif annotator_aggregation == "majority":
          annotations = {}
          for annotator in self._annotations:
              for change_point in self._annotations[annotator]:
                  if change_point in annotations:
                      annotations[change_point] += 1
                  else:
                      annotations[change_point] = 1
          annotations = {
              change_point
              for change_point, count in annotations.items()
              if count > len(self._annotations) / 2
          }
      else:
          raise ValueError("Unknown annotator aggregation method.")
      return annotations
  
file_path: river/river/drift/datasets/base.py
imports: from __future__ import annotations
  import abc
  import pathlib
  from river.datasets import base
label_desc: Use image node_id 3 for calling the ChangePointFileDataset obj's underlying member method code with example usage: obj._annotations_aggregated(annotator_aggregation) and returns: annotations